{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:23:25Z","timestamp":1792790605397,"version":"4.5.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81930024"],"award-info":[{"award-number":["81930024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271122"],"award-info":[{"award-number":["82271122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Commission of Shanghai","award":["20DZ2270800"],"award-info":[{"award-number":["20DZ2270800"]}]},{"name":"Shanghai Key Clinical Specialty, Shanghai Eye Disease Research Center","award":["2022ZZ01003"],"award-info":[{"award-number":["2022ZZ01003"]}]},{"name":"Clinical Acceleration Program of Shanghai Ninth People\u2019s Hospital, Shanghai Jiao Tong University School of Medicine","award":["JYLJ202202"],"award-info":[{"award-number":["JYLJ202202"]}]},{"name":"Cross disciplinary Research Fund of Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine","award":["JYJC202115"],"award-info":[{"award-number":["JYJC202115"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Transl Med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Radiomics analysis of orbital magnetic resonance imaging (MRI) shows preliminary potential for intravenous glucocorticoid (IVGC) response prediction of thyroid eye disease (TED). The current region of interest segmentation contains only a single organ as extraocular muscles (EOMs). It would be of great value to consider all orbital soft tissues and construct a better prediction model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this retrospective study, we enrolled 127 patients with TED that received 4\u00b75\u00a0g IVGC therapy and had complete follow-up examinations. Pre-treatment orbital T2-weighted imaging (T2WI) was acquired for all subjects. Using multi-organ segmentation (MOS) strategy, we contoured the EOMs, lacrimal gland (LG), orbital fat (OF), and optic nerve (ON), respectively. By fused-organ segmentation (FOS), we contoured the aforementioned structures as a cohesive unit. Whole-orbit radiomics (WOR) models consisting of a multi-regional radiomics (MRR) model and a fused-regional radiomics (FRR) model were further constructed using six machine learning (ML) algorithms.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The support vector machine (SVM) classifier had the best performance on the MRR model (AUC\u2009=\u20090\u00b7961). The MRR model outperformed the single-regional radiomics (SRR) models (highest AUC\u2009=\u20090\u00b7766, XGBoost on EOMs, or LR on OF) and conventional semiquantitative imaging model (highest AUC\u2009=\u20090\u00b7760, NaiveBayes). The application of different ML algorithms for the comparison between the MRR model and the FRR model (highest AUC\u2009=\u20090\u00b7916, LR) led to different conclusions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The WOR models achieved a satisfactory result in IVGC response prediction of TED. It would be beneficial to include more orbital structures and implement ML algorithms while constructing radiomics models. The selection of separate or overall segmentation of orbital soft tissues has not yet attained its final optimal result.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12967-023-04792-2","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T23:02:04Z","timestamp":1705186924000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease"],"prefix":"10.1186","volume":"22","author":[{"given":"Haiyang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengda","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoi Chi","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijie","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiashuo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofeng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duojin","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuefei","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8636-861X","authenticated-orcid":false,"given":"Huifang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianqun","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"issue":"4","key":"4792_CR1","doi-asserted-by":"publisher","first-page":"G43","DOI":"10.1530\/EJE-21-0479","volume":"185","author":"L Bartalena","year":"2021","unstructured":"Bartalena L, Kahaly GJ, Baldeschi L, Dayan CM, Eckstein A, Marcocci C, et al. The 2021 European group on graves\u2019 orbitopathy (EUGOGO) clinical practice guidelines for the medical management of Graves\u2019 orbitopathy. Eur J Endocrinol. 2021;185(4):G43-67.","journal-title":"Eur J Endocrinol"},{"issue":"8","key":"4792_CR2","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1056\/NEJMra0905750","volume":"362","author":"RS Bahn","year":"2010","unstructured":"Bahn RS. Graves\u2019 ophthalmopathy. N Engl J Med. 2010;362(8):726\u201338.","journal-title":"N Engl J Med"},{"issue":"2","key":"4792_CR3","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41574-019-0305-4","volume":"16","author":"PN Taylor","year":"2020","unstructured":"Taylor PN, Zhang L, Lee RWJ, Muller I, Ezra DG, Dayan CM, et al. New insights into the pathogenesis and nonsurgical management of graves orbitopathy. Nat Rev Endocrinol. 2020;16(2):104\u201316.","journal-title":"Nat Rev Endocrinol"},{"issue":"9","key":"4792_CR4","first-page":"646","volume":"58","author":"Oculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association","year":"2022","unstructured":"Oculoplastic and Orbital Disease Group of Chinese Ophthalmological Society of Chinese Medical Association. Thyroid group of Chinese society of endocrinology of chinese medical association. Trends Endocrinol Metab. 2022;58(9):646\u201368.","journal-title":"Trends Endocrinol Metab"},{"issue":"1","key":"4792_CR5","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.tem.2017.10.010","volume":"29","author":"J Vandewalle","year":"2018","unstructured":"Vandewalle J, Luypaert A, De Bosscher K, Libert C. Therapeutic mechanisms of glucocorticoids. Trends Endocrinol Metab. 2018;29(1):42\u201354.","journal-title":"Trends Endocrinol Metab"},{"issue":"1","key":"4792_CR6","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1530\/EJE-13-0611","volume":"170","author":"G Vannucchi","year":"2014","unstructured":"Vannucchi G, Covelli D, Campi I, Origo D, Curr\u00f2 N, Cirello V, et al. The therapeutic outcome to intravenous steroid therapy for active Graves\u2019 orbitopathy is influenced by the time of response but not polymorphisms of the glucocorticoid receptor. Eur J Endocrinol. 2014;170(1):55\u201361.","journal-title":"Eur J Endocrinol"},{"issue":"12","key":"4792_CR7","doi-asserted-by":"publisher","first-page":"4454","DOI":"10.1210\/jc.2012-2389","volume":"97","author":"L Bartalena","year":"2012","unstructured":"Bartalena L, Krassas GE, Wiersinga W, Marcocci C, Salvi M, Daumerie C, et al. Efficacy and safety of three different cumulative doses of intravenous methylprednisolone for moderate to severe and active graves\u2019 orbitopathy. J Clin Endocrinol Metab. 2012;97(12):4454\u201363.","journal-title":"J Clin Endocrinol Metab"},{"issue":"2","key":"4792_CR8","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/S2213-8587(16)30046-8","volume":"5","author":"WM Wiersinga","year":"2016","unstructured":"Wiersinga WM. Advances in treatment of active, moderate-to-severe graves\u2019 ophthalmopathy. Lancet Diabetes Endocrinol. 2016;5(2):134\u201342.","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"5","key":"4792_CR9","first-page":"339","volume":"27","author":"H Jiang","year":"2018","unstructured":"Jiang H, Yan F, Xian J, Ai L, Wang X. T2 mapping MRI study on immunosuppression therapy of thyroid-associated ophthalmopathy. Ophthalmol China. 2018;27(5):339.","journal-title":"Ophthalmol China"},{"issue":"12","key":"4792_CR10","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.4158\/EP-2019-0133","volume":"25","author":"M Zhou","year":"2019","unstructured":"Zhou M, Shen L, Jiao Q, Ye L, Zhou Y, Zhu W, et al. Role of magnetic resonance imaging in the assessment of active thyroid-associated ophthalmopathy patients with long disease duration. Endocr Pract. 2019;25(12):1268\u201378.","journal-title":"Endocr Pract"},{"issue":"3","key":"4792_CR11","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1089\/105072502753600179","volume":"12","author":"N Yokoyama","year":"2002","unstructured":"Yokoyama N, Nagataki S, Uetani M, Ashizawa K, Eguchi K. Role of magnetic resonance imaging in the assessment of disease activity in thyroid-associated ophthalmopathy. Thyroid. 2002;12(3):223\u20137.","journal-title":"Thyroid"},{"key":"4792_CR12","doi-asserted-by":"crossref","unstructured":"Zhang H, Lu T, Liu Y, Jiang M, Wang Y, Song X, et al. Application of quantitative MRI in thyroid eye disease: imaging techniques and clinical practices. J Magn Reson Imaging. 2023.","DOI":"10.1002\/jmri.29114"},{"issue":"3","key":"4792_CR13","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1148\/radiology.172.3.2772184","volume":"172","author":"N Hosten","year":"1989","unstructured":"Hosten N, Sander B, Cordes M, Schubert CJ, Sch\u00f6rner W, Felix R. Graves ophthalmopathy: MR imaging of the orbits. Radiology. 1989;172(3):759\u201362.","journal-title":"Radiology"},{"issue":"3","key":"4792_CR14","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1002\/jmri.28088","volume":"56","author":"H Hu","year":"2022","unstructured":"Hu H, Chen L, Zhang JL, Chen W, Chen HH, Liu H, et al. T2-weighted MR imaging-derived radiomics for pretreatment determination of therapeutic response to glucocorticoid in patients with thyroid-associated ophthalmopathy: comparison with semiquantitative evaluation. J Magn Reson Imaging. 2022;56(3):862\u201372.","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"4792_CR15","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G, H\u00e4ggstr\u00f6m I, Szczypi\u0144ski P, Gibbs P, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488\u201395.","journal-title":"J Nucl Med"},{"key":"4792_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2021.101201","volume":"42","author":"S Fu","year":"2021","unstructured":"Fu S, Lai H, Huang M, Li Q, Liu Y, Zhang J, et al. Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma. EClinicalMedicine. 2021;42: 101201.","journal-title":"EClinicalMedicine"},{"key":"4792_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2021.101215","volume":"43","author":"X Li","year":"2021","unstructured":"Li X, Wan Y, Lou J, Xu L, Shi A, Yang L, et al. Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography. EClinicalMedicine. 2021;43: 101215.","journal-title":"EClinicalMedicine"},{"issue":"3","key":"4792_CR18","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1097\/RLI.0000000000000722","volume":"56","author":"L Duron","year":"2021","unstructured":"Duron L, Heraud A, Charbonneau F, Zmuda M, Savatovsky J, Fournier L, et al. A magnetic resonance imaging radiomics signature to distinguish benign from malignant orbital lesions. Invest Radiol. 2021;56(3):173\u201380.","journal-title":"Invest Radiol"},{"issue":"2","key":"4792_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s12020-022-03167-9","volume":"78","author":"M Jiang","year":"2022","unstructured":"Jiang M, Song X, Zhang H, Tao X, Yang G, Wang Y, et al. The combination of T2-mapping value of lacrimal gland and clinical indicators can improve the stage prediction of graves\u2019 ophthalmopathy compared to clinical activity scores. Endocrine. 2022;78(2):321\u20138.","journal-title":"Endocrine"},{"key":"4792_CR20","doi-asserted-by":"publisher","first-page":"1001349","DOI":"10.3389\/fendo.2022.1001349","volume":"13","author":"J Huang","year":"2022","unstructured":"Huang J, Chen M, Liang Y, Hu Y, Xia W, Zhang Y, et al. Integrative metabolic analysis of orbital adipose\/connective tissue in patients with thyroid-associated ophthalmopathy. Front Endocrinol. 2022;13:1001349.","journal-title":"Front Endocrinol"},{"key":"4792_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.895186","volume":"13","author":"P Zhu","year":"2022","unstructured":"Zhu P, Liu Z, Lu Y, Wang Y, Zhang D, Zhao P, et al. Alterations in spontaneous neuronal activity and microvascular density of the optic nerve head in active thyroid-associated ophthalmopathy. Front Endocrinol. 2022;13: 895186.","journal-title":"Front Endocrinol"},{"key":"4792_CR22","doi-asserted-by":"publisher","first-page":"3196059","DOI":"10.1155\/2017\/3196059","volume":"2017","author":"L Xu","year":"2017","unstructured":"Xu L, Li L, Xie C, Guan M, Xue Y. Thickness of extraocular muscle and orbital fat in mri predicts response to glucocorticoid therapy in graves\u2019 ophthalmopathy. Int J Endocrinol. 2017;2017:3196059.","journal-title":"Int J Endocrinol"},{"issue":"2","key":"4792_CR23","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/s11548-020-02281-1","volume":"16","author":"X Song","year":"2020","unstructured":"Song X, Liu Z, Li L, Gao Z, Fan X, Zhai G, et al. Artificial intelligence CT screening model for thyroid-associated ophthalmopathy and tests under clinical conditions. Int J Comput Assist Radiol Surg. 2020;16(2):323\u201330.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"4792_CR24","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/s12020-020-02367-5","volume":"70","author":"H Hu","year":"2020","unstructured":"Hu H, Xu XQ, Chen L, Chen W, Wu Q, Chen HH, et al. Predicting the response to glucocorticoid therapy in thyroid-associated ophthalmopathy: mobilizing structural MRI-based quantitative measurements of orbital tissues. Endocrine. 2020;70(2):372\u20139.","journal-title":"Endocrine"},{"issue":"1","key":"4792_CR25","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1002\/jmri.28498","volume":"58","author":"L Zhang","year":"2022","unstructured":"Zhang L, Li X, Yang L, Tang Y, Guo J, Li D, et al. Multi-sequence and multi-regional MRI-based radiomics nomogram for the preoperative assessment of muscle invasion in bladder cancer. J Magn Reson Imaging. 2022;58(1):258\u201369.","journal-title":"J Magn Reson Imaging"},{"key":"4792_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103373","volume":"72","author":"J Shi","year":"2022","unstructured":"Shi J, Cui L, Wang H, Dong Y, Yu T, Yang H, et al. MRI-based intratumoral and peritumoral radiomics on prediction of lymph-vascular space invasion in cervical cancer: a multi-center study. Biomed Signal Process Control. 2022;72: 103373.","journal-title":"Biomed Signal Process Control"},{"key":"4792_CR27","doi-asserted-by":"publisher","first-page":"614536","DOI":"10.3389\/fendo.2021.614536","volume":"12","author":"P Liu","year":"2021","unstructured":"Liu P, Luo B, Chen L, Wang QX, Yuan G, Jiang G, et al. Baseline volumetric T2 relaxation time histogram analysis: can it be used to predict the response to intravenous methylprednisolone therapy in patients with thyroid-associated ophthalmopathy? Front Endocrinol. 2021;12:614536.","journal-title":"Front Endocrinol"},{"issue":"2","key":"4792_CR28","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1097\/WNO.0000000000000128","volume":"34","author":"MT Bhatti","year":"2014","unstructured":"Bhatti MT, Dutton JJ. Thyroid eye disease: therapy in the active phase. J Neuroophthalmol. 2014;34(2):186\u201397.","journal-title":"J Neuroophthalmol"},{"issue":"11","key":"4792_CR29","doi-asserted-by":"publisher","first-page":"4760","DOI":"10.1167\/iovs.08-2020","volume":"49","author":"MH Chen","year":"2008","unstructured":"Chen MH, Liao SL, Chen MH, Tsou PL, Shih MJ, Chang TC, et al. Lysosome-related genes are regulated in the orbital fat of patients with graves ophthalmopathy. Invest Ophthalmol Vis Sci. 2008;49(11):4760\u20134.","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"9","key":"4792_CR30","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1167\/iovs.62.9.24","volume":"62","author":"DW Kim","year":"2021","unstructured":"Kim DW, Taneja K, Hoang T, Santiago CP, McCulley TJ, Merbs SL, et al. Transcriptomic profiling of control and thyroid-associated orbitopathy (TAO) orbital fat and TAO orbital fibroblasts undergoing adipogenesis. Invest Ophth Vis Sci. 2021;62(9):24.","journal-title":"Invest Ophth Vis Sci"},{"issue":"5","key":"4792_CR31","first-page":"423","volume":"44","author":"Y Hei","year":"2008","unstructured":"Hei Y, Kang L, Li YY, Xiao LH. The pathological changes of the orbital tissue in thyroid-associated ophthalmopathy. Zhonghua Yan Ke Za Zhi. 2008;44(5):423\u20136.","journal-title":"Zhonghua Yan Ke Za Zhi"},{"issue":"2","key":"4792_CR32","doi-asserted-by":"publisher","first-page":"725","DOI":"10.3892\/etm.2016.3389","volume":"12","author":"H Hu","year":"2016","unstructured":"Hu H, Xu XQ, Wu FY, Chen HH, Su GY, Shen J, et al. Diagnosis and stage of graves\u2019 ophthalmopathy: efficacy of quantitative measurements of the lacrimal gland based on 3-T magnetic resonance imaging. Exp Ther Med. 2016;12(2):725\u20139.","journal-title":"Exp Ther Med"},{"issue":"3","key":"4792_CR33","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1007\/s00330-021-08300-2","volume":"32","author":"M Zou","year":"2021","unstructured":"Zou M, Wu D, Zhu H, Huang X, Zhao X, Zhao J, et al. Multiparametric quantitative MRI for the evaluation of dysthyroid optic neuropathy. Eur Radiol. 2021;32(3):1931\u20138.","journal-title":"Eur Radiol"},{"issue":"9","key":"4792_CR34","doi-asserted-by":"publisher","first-page":"4497","DOI":"10.1109\/JBHI.2022.3181791","volume":"26","author":"M Meng","year":"2022","unstructured":"Meng M, Gu B, Bi L, Song S, Feng DD, Kim J. DeepMTS: deep multi-task learning for survival prediction in patients with advanced nasopharyngeal carcinoma using pretreatment PET\/CT. IEEE J Biomed Health Inform. 2022;26(9):4497\u2013507.","journal-title":"IEEE J Biomed Health Inform"},{"key":"4792_CR35","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA: ACM; 2016. p. 785\u201394. https:\/\/dl.acm.org\/doi\/https:\/\/doi.org\/10.1145\/2939672.2939785. Accessed 9 Nov 2023.","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"4792_CR36","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273\u201397.","journal-title":"Mach Learn"}],"container-title":["Journal of Translational Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-023-04792-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12967-023-04792-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12967-023-04792-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T15:02:56Z","timestamp":1705330976000},"score":1,"resource":{"primary":{"URL":"https:\/\/translational-medicine.biomedcentral.com\/articles\/10.1186\/s12967-023-04792-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["4792"],"URL":"https:\/\/doi.org\/10.1186\/s12967-023-04792-2","relation":{},"ISSN":["1479-5876"],"issn-type":[{"value":"1479-5876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"5 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was approved by our Institutional Review Board (SH9H-2021-T246-2), and the requirement for informed consent was waived.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have approved the manuscript for submission.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no potential competing interests related to this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"56"}}